Dealing with empty frames in MRI images
I've worked on the BRATS dataset and I can verify that this is pretty much standard process. Besides throwing the totally blank images, I also throw away the images in the beginning and ending of the sequence that show the tip of the scull and the base of the neck.

Generally when dealing with MRIs,
I do this with a script (think of is as a preprocessing step) I run on each image that counts the amount of pixels that have a positive intensity (I actually add a small value to this to account for noise). Let's say for images with values 0-255 I count the amount of pixels with an intensity of over 10-15, let's call this $x$. After that I set a threshold (empirically), let's call it $t$ and discard images with $x &lt; t$. 

Specifically for BRATS, because you have the labels, you can see which of these have the desired classes and discard most of the rest. If you try to train a network on the dataset as is you face an enormous imbalance ratio. I've had trouble training networks due to this and the most success I got was when I threw away most of the irrelevant images.